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Pricing

Remember the scientific method in school? It’s a six-step process that helps answer a looming question.

In the frame of ecommerce, it can answer “what’s the right price for my products?”

Building an algorithm is a process that varies greatly, depending on its application but it boils down to a few steps: measuring different variables, applying various statistical methods to get the best fit, testing it, and then distributing it across your entire product catalog.

A couple of variables taken into a pricing algorithm’s consideration are product seasonality, elasticity to competitor prices, and desired margin.

Using these factors, you can then build a demand estimation engine (which is actually exactly what it sounds like.)

You use this demand estimation engine to hypothesize what would happen with different price changes in your product catalog. You then test this hypothesis on a sample of SKUs, and measure the results.

To validate these tests, you can expand the algorithm to the rest of your catalog and analyze results.

Constantly changing these algorithms until you establish a high confidence level is where the fun really begins.

For example, let’s say you introduce a new catalog of products that you’ve never sold before. Algorithms are capable of taking that new assortment, identifying similar characteristics of products you have sold and estimating demand.

Over time, your algorithm learns how to optimize these products for revenue or profit, avoiding the common pitfalls of a more manual approach.

Inventory forecasting

But what about other aspects of your store that are directly related to demand levels? Like inventory, for example.

You don’t want to have the right price for your product, win the shopper’s interest, but lead them to a page that says you’re out of stock.

Machine learning algorithms can act as retail meteorologists, giving you a forecast for your inventory levels using their demand estimation engines.

Forecasting demand can help you order the right amount of stock to last you through any rises or dips in traffic to your site.

Using a set of factors similar to pricing algorithms, you can estimate demand and order your store’s items accordingly.

Then, after building confidence, your algorithm can learn what works well for your inventory levels and what hurts your bottom line.

So what are these algorithms doing for the retail industry? In a nutshell, they’re giving retailers a more cost-friendly approach to improving their competitive levels and earning a profit.

They are giving retailers the ability to reprice like the “king” of the ecommerce jungle, Amazon.

Cost reduction

The simplicity of machine learning all translates into reduced costs for your business. Instead of paying the salaries of 10 workers to do tedious work, you could automatically monitor and implement algorithms that continuously optimize your ecommerce store and stock levels.

In place of doing hours of manual work, you can have an algorithm do all the heavy lifting. This automation gives you the opportunity to improve other aspects of your business.

This means more flexibility to improve the shopping experience, which in turn can strengthen your brand value in the eyes of your shoppers.

In summary…

There’s a lot in store for retail in 2016, and I think machine learning algorithms are one of the most powerful tools a retailer should use to get ahead in the growing industry.

Shoppers are expecting a more personalized shopping experience free of speed bumps that can run rampant in online retail.

Algorithms can do the heavy pricing work for your business, and let you add a human touch to improve your store’s shopping experience.

They unlock what may have been a previously unknown potential to become a leader. Therefore you’re never settling for a mediocre store, you’re improving every aspect of it past the point you may have known was possible.

Companies are pouring billions of dollars a year into social media and influencer marketing campaigns, many of which target consumers on Facebook-owned Instagram, in an effort to parlay social engagement into sales.